Add Pytorch implementation of Blockwise

pytensor/link/pytorch/dispatch/__init__.py

@@ -11,4 +11,5 @@
 import pytensor.link.pytorch.dispatch.shape
 import pytensor.link.pytorch.dispatch.sort
 import pytensor.link.pytorch.dispatch.subtensor
+import pytensor.link.pytorch.dispatch.blockwise
 # isort: on

pytensor/link/pytorch/dispatch/blockwise.py

@@ -0,0 +1,32 @@
+import torch
+import torch.compiler
+
+from pytensor.graph import FunctionGraph
+from pytensor.link.pytorch.dispatch import pytorch_funcify
+from pytensor.tensor.blockwise import Blockwise
+
+
+@pytorch_funcify.register(Blockwise)
+def funcify_Blockwise(op: Blockwise, node, *args, **kwargs):
+    batched_dims = op.batch_ndim(node)
+    core_node = op._create_dummy_core_node(node.inputs)
+    core_fgraph = FunctionGraph(inputs=core_node.inputs, outputs=core_node.outputs)
+    inner_func = pytorch_funcify(core_fgraph, squeeze_output=len(node.outputs) == 1)
+
+    for _ in range(batched_dims):
+        inner_func = torch.vmap(inner_func)
+
+    @torch.compiler.disable(recursive=False)
+    def batcher(*inputs):
+        op._check_runtime_broadcast(node, inputs)
+        # broadcast on batched_dims
+        all_batched_dims = tuple(t.shape[:batched_dims] for t in inputs)
+        batched_shape = torch.broadcast_shapes(*all_batched_dims)
+        broadcast_inputs = [
+            torch.broadcast_to(i, batched_shape + i.shape[batched_dims:])
+            for i in inputs
+        ]
+        res = inner_func(*broadcast_inputs)
+        return res
+
+    return batcher

tests/link/pytorch/test_blockwise.py

@@ -0,0 +1,53 @@
+import numpy as np
+import pytest
+
+import pytensor
+import pytensor.tensor as pt
+from pytensor.graph.basic import Apply
+from pytensor.graph.op import Op
+from pytensor.tensor.blockwise import Blockwise
+
+
+torch = pytest.importorskip("torch")
+basic = pytest.importorskip("pytensor.link.pytorch.dispatch.basic")
+
+
+class TestOp(Op):
+    gufunc_signature = "(m,n),(n,p)->(m,p)"
+
+    def __init__(self, final_shape):
+        super().__init__()
+        self.final_shape = final_shape
+        self.call_shapes = []
+
+    def make_node(self, *args):
+        return Apply(self, list(args), [pt.matrix("_", shape=self.final_shape)])
+
+    def perform(self, *_):
+        raise RuntimeError("In perform")
+
+
+@basic.pytorch_funcify.register(TestOp)
+def evaluate_test_op(op, **_):
+    @torch.compiler.disable(recursive=False)
+    def func(a, b):
+        op.call_shapes.extend(map(torch.Tensor.size, [a, b]))
+        return a @ b
+
+    return func
+
+
+def test_blockwise_broadcast():
+    _x = np.random.rand(5, 1, 2, 3)
+    _y = np.random.rand(3, 3, 2)
+
+    x = pt.tensor4("x", shape=(5, 1, 2, 3))
+    y = pt.tensor3("y", shape=(3, 3, 2))
+    op = TestOp((2, 2))
+    z = Blockwise(op)(x, y)
+
+    f = pytensor.function([x, y], z, mode="PYTORCH")
+    res = f(_x, _y)
+    assert tuple(res.shape) == (5, 3, 2, 2)
+    np.testing.assert_allclose(res, _x @ _y)
+    assert op.call_shapes == [(2, 3), (3, 2)]

tests/tensor/test_blockwise.py

@@ -538,7 +538,7 @@ def core_scipy_fn(A, b):
             A_val_copy, b_val_copy
         )
         np.testing.assert_allclose(
-            out, expected_out, atol=1e-6 if config.floatX == "float32" else 0
+            out, expected_out, atol=1e-5 if config.floatX == "float32" else 0
         )
 
         # Confirm input was destroyed